kobart-base-v2-finetuned-paper

This model is a fine-tuned version of gogamza/kobart-base-v2 on the aihub_paper_summarization dataset. It achieves the following results on the evaluation set:

• Loss: 1.2966
• Rouge1: 6.2883
• Rouge2: 1.7038
• Rougel: 6.2556
• Rougelsum: 6.2618
• Gen Len: 20.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.2215	1.0	8831	1.3293	6.2425	1.7317	6.2246	6.2247	20.0
1.122	2.0	17662	1.3056	6.2298	1.7005	6.2042	6.2109	20.0
1.0914	3.0	26493	1.2966	6.2883	1.7038	6.2556	6.2618	20.0

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.1
• Datasets 2.8.0
• Tokenizers 0.13.2